%matplotlib inline
# OPTIONAL: Load the "autoreload" extension so that code can change
%load_ext autoreload
# OPTIONAL: always reload modules so that as you change code in src, it gets loaded
%autoreload 2

Please enter a brief description of the model, considering that more detailed information will be entered in the following sections
import pandas as pd
input_parameters = pd.read_csv('../data/processed/cultural-e-input.csv')
Please enter the following general information related to the building
| Field | Value |
|---|---|
| Type of building | - |
| Location | - |
| Number of thermal zones | - |
| Photovoltaic system | - |
| Technology installed | - |
| Position [façade or roof] | - |
| Azimuth [°] | - |
| Space for additional information/system | - |
| Quantity | ID |
|---|---|
| Gross floor area [m2] | IN_GFA |
| Net floor area [m2] | IN_NIA |
| S/V ratio | IN_SV |
| PV capacity [kWp] | IN_PV_kWp |
| PV area [m2] | IN_A_PV |
| Battery capacity [kWh] | IN_PV_bat |
| Tilt angle [°] | IN_PV_Tilt |
input_parameters[[
'IN_GFA', 'IN_NIA', 'IN_SV', 'IN_A_PV', 'IN_PV_bat',
'IN_PV_Tilt'
]]
Please fill in this table for the information related to the thermal zones. This information must be provided for each thermal zone.
Please fill in this table with the general information required on the internal gains.
Please consider adding the heating setpoint schedule chart.
Please consider adding the cooling setpoint schedule chart.
Please consider adding the occupancy schedule chart.
Please consider adding the lighting schedule chart.
Please consider adding the appliances schedule chart.
Using the following sintax:

Please fill in the following table with the information related to the opaque envelope components. Please enter manually the entire table except for the section “U-value [W/m²K]”
from pvlib.iotools import read_epw
weather, meta = read_epw('../data/processed/meteo.epw')
weather.head()
summary = pd.read_csv('../data/processed/summary.csv')
summary.head()
energy_zones = pd.read_csv('../data/processed/energy_zones.csv')
energy_zones.head()
cultural_e = pd.read_csv('../data/processed/cultural-e.csv')
cultural_e.head()
from src.visualization import visualize as viz
The weather conditions of a location play an important role in the energy performance of a building. In the next subsections, some results in terms of outdoor air temperature, global horizontal irradiance, and relative humidity, are presented.
viz.air_temperature(weather)
| - | - |
|---|---|
| Description of the graph | Hourly dry-bulb temperature distribution and the cumulative frequency of a standard year. |
| Interpretation of results | [Please enter manually this field] |
viz.relative_humidity(weather)
viz.horizontal_irradiance(weather)
This section shows the main results in terms of energy balance of the building, energy consumption considering the total energy use of the house and overall heating load considering an ideal heating and/or cooling system. It also gives information on the use of renewable energy in case a photovoltaic system has been installed in the building.
viz.heating_loads(cultural_e)
viz.cooling_loads(cultural_e)
viz.energy_balance(summary)
for zone in ['1', '2', '3', '4', '5']:
viz.zone_energy_balance(energy_zones, zone)
viz.monthly_consumption(cultural_e)
viz.self_production_consumption(cultural_e)
This section has been organized to show the main results in terms of thermal comfort, visual comfort and IAQ. For each group of output, you will be asked to enter the results for each thermal zone. If you think it is useful to assess the comfort in only some areas of the building or only in one, enter the results only for those useful for your evaluation.
viz.airt_heatmap(cultural_e, 'F1dayA')
viz.psychrochart(cultural_e.sample(n=1000), 'F1dayA', weather.sample(n=1000)).get_figure()
In order to assess the air quality of the building during the occupied time, considering the people as one of the main pollution sources, the level of the CO2 concentration generated by the occupants, need to be calculated. The limits for indoor CO2 concentrations leading to the four IAQ categories have been calculated in accordance with the standard EN 16798-1: 2019.
viz.iaq_co2(cultural_e, ['F1dayA'], ['F1nightA1', 'F1nightA2'])
Another necessary parameter to evaluate the internal comfort of a building is the indoor relative humidity. This is important because high or low percentages lead to humid or dry environment, respectively, which has a direct effect on human well-being. An effective way to evaluate this data is to classify the number of hours in which the relative humidity of a thermal zone falls within the categories for humidification and dehumidification, identified in standard EN 16798-1: 2019.
viz.relh(cultural_e, ['RELHUM_F1dayA', 'RELHUM_F1nightA1', 'RELHUM_F1nightA2'], ['OCC_F1dayA', 'OCC_F1nightA1', 'OCC_F1nightA2'])
The frequency of opening the windows can be considered an interesting indicator in the evaluation of some aspects related to the performance of the building. thanks to this result it is possible to evaluate, for example, whether the action of natural ventilation alone can guarantee an acceptable level of internal comfort, whether it affects the energy consumption of the building as well as giving indications on how the occupants interact with the building.
viz.win_heatmap(cultural_e, 'F1dayA')
viz.shd_heatmap(cultural_e, 'F1dayA')